OpenRaiser/Intern-Atlas huggingface dataset:
A Hybrid Deep Learning and Anomaly Detection Framework for Real-Time Malicious URL Classification
Venue: arXiv cs.CR 2025
Year: 2025

Malicious URLs remain a primary vector for phishing, malware, and cyberthreats. This study proposes a hybrid deep learning framework combining \texttt{HashingVectorizer} n-gram analysis, SMOTE balancing, Isolation Forest anomaly filtering, and a lightweight neural network classifier for real-time URL classification. The multi-stage pipeline processes URLs from open-source repositories with statistical features (length, dot count, entropy), achieving $O(NL + EBdh)$ training complexity and a 20\,ms prediction latency. Empirical evaluation yields 96.4\% accuracy, 95.4\% F1-score, and 97.3\% ROC-AUC, outperforming CNN (94.8\%) and SVM baselines with a $50\!\times$--$100\!\times$ speedup (Table~\ref{tab:comp-complexity}). A multilingual Tkinter GUI (Arabic/English/French) enables real-time threat assessment with clipboard integration. The framework demonstrates superior scalability and resilience against obfuscated URL patterns.
A Preliminary Study on the Promises and Challenges of Native Top-$k$ Sparse Attention
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) are increasingly prevalent in the field of long-context modeling, however, their inference computational costs have become a critical bottleneck hindering the advancement of tasks such as agents and multimodal applications. This report conducts a preliminary investigation into the effectiveness and theoretical mechanisms of the Top-$k$ Attention mechanism during both the decoding and training phases. First, we validate the effectiveness of exact Top-$k$ Decoding through extensive experimentation. Experiments demonstrate that retaining only the pivotal Keys with the highest similarity to the Query as the context window during the decoding stage achieves performance comparable to, or even surpassing, full attention on downstream tasks such as HELMET and LongBench v2. Second, we further explore the native Top-$k$ Attention training strategy. Experiments confirm that ensuring the consistency between training and inference regarding Top-$k$ Attention operations facilitates the further unlocking of Top-$k$ Decoding's potential, thereby significantly enhancing model performance. Furthermore, considering the high computational complexity of exact Top-$k$ Attention, we investigate the impact of approximate Top-$k$ algorithm precision on downstream tasks. Our research confirms a positive correlation between downstream task performance and approximation fidelity, and we provide statistical evaluations of the Lightning Indexer's precision within the DeepSeek-V3.2-Exp model. Finally, this report provides a theoretical interpretation from the perspective of Entropy. Experimental observations indicate that models subjected to Top-$k$ Attention SFT exhibit a distinct phenomenon of entropy reduction in downstream tasks, which validates the hypothesis that low-entropy states are better adapted to Top-$k$ Decoding.
Understanding LLM Reasoning for Abstractive Summarization
Venue: arXiv cs.CL 2025
Year: 2025

While the reasoning capabilities of Large Language Models (LLMs) excel in analytical tasks such as mathematics and code generation, their utility for abstractive summarization remains widely assumed but largely unverified. To bridge this gap, we first tailor general reasoning strategies to the summarization domain. We then conduct a systematic, large scale comparative study of 8 reasoning strategies and 3 Large Reasoning Models (LRMs) across 8 diverse datasets, assessing both summary quality and faithfulness. Our findings show that reasoning is not a universal solution and its effectiveness is highly dependent on the specific strategy and context. Specifically, we observe a trade-off between summary quality and factual faithfulness: explicit reasoning strategies tend to improve fluency at the expense of factual grounding, while implicit reasoning in LRMs exhibits the inverse pattern. Furthermore, increasing an LRM's internal reasoning budget does not improve, and can even hurt, factual consistency, suggesting that effective summarization demands faithful compression rather than creative over-thinking.
CSMapping: Scalable Crowdsourced Semantic Mapping and Topology Inference for Autonomous Driving
Venue: arXiv cs.CV 2025
Year: 2025

Crowdsourcing enables scalable autonomous driving map construction, but low-cost sensor noise hinders quality from improving with data volume. We propose CSMapping, a system that produces accurate semantic maps and topological road centerlines whose quality consistently increases with more crowdsourced data. For semantic mapping, we train a latent diffusion model on HD maps (optionally conditioned on SD maps) to learn a generative prior of real-world map structure, without requiring paired crowdsourced/HD-map supervision. This prior is incorporated via constrained MAP optimization in latent space, ensuring robustness to severe noise and plausible completion in unobserved areas. Initialization uses a robust vectorized mapping module followed by diffusion inversion; optimization employs efficient Gaussian-basis reparameterization, projected gradient descent zobracket multi-start, and latent-space factor-graph for global consistency. For topological mapping, we apply confidence-weighted k-medoids clustering and kinematic refinement to trajectories, yielding smooth, human-like centerlines robust to trajectory variation. Experiments on nuScenes, Argoverse 2, and a large proprietary dataset achieve state-of-the-art semantic and topological mapping performance, with thorough ablation and scalability studies.
Left shifting analysis of Human-Autonomous Team interactions to analyse risks of autonomy in high-stakes AI systems
Venue: arXiv cs.HC 2025
Year: 2025

Developing high-stakes autonomous systems that include Artificial Intelligence (AI) components is complex; the consequences of errors can be catastrophic, yet it is challenging to plan for all operational cases. In stressful scenarios for the human operator, such as short decision-making timescales, the risk of failures is exacerbated. A lack of understanding of AI failure modes obstructs this and so blocks the robust implementation of applications of AI in smart systems. This prevents early risk identification, leading to increased time, risk and cost of projects. A key tenet of Systems Engineering and acquisition engineering is centred around a "left-shift" in test and evaluation activities to earlier in the system lifecycle, to allow for "accelerated delivery of [systems] that work". We argue it is therefore essential that this shift includes the analysis of AI failure cases as part of the design stages of the system life cycle. Our proposed framework enables the early characterisation of risks emerging from human-autonomy teaming (HAT) in operational contexts. The cornerstone of this is a new analysis of AI failure modes, built on the seminal modelling of human-autonomy teams laid out by LaMonica et al., 2022. Using the analysis of the interactions between human and autonomous systems and exploring the failure modes within each aspect, our approach provides a way to systematically identify human-AI interactions risks across the operational domain of the system of interest. The understanding of the emergent behaviour enables increased robustness of the system, for which the analysis should be undertaken over the whole scope of its operational design domain. This approach is illustrated through an example use case for an AI assistant supporting a Command & Control (C2) System.
V-ITI: Mitigating Hallucinations in Multimodal Large Language Models via Visual Inference-Time Intervention
Venue: arXiv cs.CV 2025
Year: 2025

Multimodal Large Language Models (MLLMs) excel in numerous vision-language tasks yet suffer from hallucinations, producing content inconsistent with input visuals, that undermine reliability in precision-sensitive domains. This issue stems from a fundamental problem of visual neglect, where models fail to adequately prioritize input images. Existing methods typically alleviate hallucinations by intervening in the attention score or output logits, focusing on "how to intervene" but overlooking the prerequisite "when to intervene", which leads to the "over-intervention" problem and subsequently introduces new hallucinations and unnecessary computational overhead. To address this gap, we first investigate the mechanism of visual neglect and reveal it can be accurately detected via head-level activation patterns in MLLMs. We thus propose V-ITI, a lightweight visual inference-time intervention framework integrating a Visual Neglect Detector that identifies visual neglect via head-level discriminative probes and a Visual Recall Intervenor that modulates activations with prestored visual activation information only when the visual neglect is detected. Extensive experiments across eight benchmarks and different MLLM families demonstrate that V-ITI consistently mitigates vision-related hallucinations while preserving general task performance.
Federated Learning and Trajectory Compression for Enhanced AIS Coverage
Venue: arXiv cs.LG 2025
Year: 2025

This paper presents the VesselEdge system, which leverages federated learning and bandwidth-constrained trajectory compression to enhance maritime situational awareness by extending AIS coverage. VesselEdge transforms vessels into mobile sensors, enabling real-time anomaly detection and efficient data transmission over low-bandwidth connections. The system integrates the M3fed model for federated learning and the BWC-DR-A algorithm for trajectory compression, prioritizing anomalous data. Preliminary results demonstrate the effectiveness of VesselEdge in improving AIS coverage and situational awareness using historical data.
CoGraM: Context-sensitive granular optimization method with rollback for robust model fusion
Venue: arXiv cs.LG 2025
Year: 2025

Merging neural networks without retraining is central to federated and distributed learning. Common methods such as weight averaging or Fisher merging often lose accuracy and are unstable across seeds. CoGraM (Contextual Granular Merging) is a multi-stage, context-sensitive, loss-based, and iterative optimization method across layers, neurons, and weight levels that aligns decisions with loss differences and thresholds and prevents harmful updates through rollback. CoGraM is an optimization method that addresses the weaknesses of methods such as Fisher and can significantly improve the merged network.
Multimodal Control of Manipulators: Coupling Kinematics and Vision for Self-Driving Laboratory Operations
Venue: arXiv cs.RO 2025
Year: 2025

Motion planning schemes are used for planning motions of a manipulator from an initial pose to a final pose during a task execution. A motion planning scheme generally comprises of a trajectory planning method and an inverse kinematic solver to determine trajectories and joints solutions respectively. In this paper, 3 motion planning schemes developed based on Jacobian methods are implemented to traverse a redundant manipulator with a coupled finger gripper through given trajectories. RRT* algorithm is used for planning trajectories and screw theory based forward kinematic equations are solved for determining joint solutions of the manipulator and gripper. Inverse solutions are computed separately using 3 Jacobian based methods such as Jacobian Transpose (JT), Pseudo Inverse (PI), and Damped Least Square (DLS) methods. Space Jacobian and manipulability measurements of the manipulator and gripper are obtained using screw theory formulations. Smoothness and RMSE error of generated trajectories and velocity continuity, acceleration profile, jerk, and snap values of joint motions are analysed for determining an efficient motion planning method for a given task. Advantages and disadvantages of the proposed motion planning schemes mentioned above are analysed using simulation studies to determine a suitable inverse solution technique for the tasks.
Formal Analysis of the Sigmoid Function and Formal Proof of the Universal Approximation Theorem
Venue: arXiv cs.LO 2025
Year: 2025

This paper presents a formalized analysis of the sigmoid function and a fully mechanized proof of the Universal Approximation Theorem (UAT) in Isabelle/HOL, a higher-order logic theorem prover. The sigmoid function plays a fundamental role in neural networks; yet, its formal properties, such as differentiability, higher-order derivatives, and limit behavior, have not previously been comprehensively mechanized in a proof assistant. We present a rigorous formalization of the sigmoid function, proving its monotonicity, smoothness, and higher-order derivatives. We provide a constructive proof of the UAT, demonstrating that neural networks with sigmoidal activation functions can approximate any continuous function on a compact interval. Our work identifies and addresses gaps in Isabelle/HOL's formal proof libraries and introduces simpler methods for reasoning about the limits of real functions. By exploiting theorem proving for AI verification, our work enhances trust in neural networks and contributes to the broader goal of verified and trustworthy machine learning.
Cyclical Temporal Encoding and Hybrid Deep Ensembles for Multistep Energy Forecasting
Venue: arXiv cs.LG 2025
Year: 2025

Accurate electricity consumption forecasting is essential for demand management and smart grid operations. This paper introduces a unified deep learning framework that integrates cyclical temporal encoding with hybrid LSTM-CNN architectures to enhance multistep energy forecasting. We systematically transform calendar-based attributes using sine cosine encodings to preserve periodic structure and evaluate their predictive relevance through correlation analysis. To exploit both long-term seasonal effects and short-term local patterns, we employ an ensemble model composed of an LSTM, a CNN, and a meta-learner of MLP regressors specialized for each forecast horizon. Using a one year national consumption dataset, we conduct an extensive experimental study including ablation analyses with and without cyclical encodings and calendar features and comparisons with established baselines from the literature. Results demonstrate consistent improvements across all seven forecast horizons, with our hybrid model achieving lower RMSE and MAE than individual architectures and prior methods. These findings confirm the benefit of combining cyclical temporal representations with complementary deep learning structures. To our knowledge, this is the first work to jointly evaluate temporal encodings, calendar-based features, and hybrid ensemble architectures within a unified short-term energy forecasting framework.
Generative AI Practices, Literacy, and Divides: An Empirical Analysis in the Italian Context
Venue: arXiv cs.CL 2025
Year: 2025

The rise of Artificial Intelligence (AI) language technologies, particularly generative AI (GenAI) chatbots accessible via conversational interfaces, is transforming digital interactions. While these tools hold societal promise, they also risk widening digital divides due to uneven adoption and low awareness of their limitations. This study presents the first comprehensive empirical mapping of GenAI adoption, usage patterns, and literacy in Italy, based on newly collected survey data from 1,906 Italian-speaking adults. Our findings reveal widespread adoption for both work and personal use, including sensitive tasks like emotional support and medical advice. Crucially, GenAI is supplanting other technologies to become a primary information source: this trend persists despite low user digital literacy, posing a risk as users struggle to recognize errors or misinformation. Moreover, we identify a significant gender divide -- particularly pronounced in older generations -- where women are half as likely to adopt GenAI and use it less frequently than men. While we find literacy to be a key predictor of adoption, it only partially explains this disparity, suggesting that other barriers are at play. Overall, our data provide granular insights into the multipurpose usage of GenAI, highlighting the dual need for targeted educational initiatives and further investigation into the underlying barriers to equitable participation that competence alone cannot explain.
Lifting the Cage of Consent: A Techno-Legal Perspective on Evolvable Trust Relationships
Venue: arXiv cs.CY 2025
Year: 2025

Those concerned about privacy worry that personal data changes hands too easily. We argue that the actual challenge is the exact opposite: our data does not flow well enough, cultivating a reliance on questionable and often unlawful shortcuts in a desperate bid to survive within today's data-driven economy. Exclusively punitive interpretations of protective legislation such as the GDPR throw out the baby with the bathwater through barriers that equally hinder "doing the right thing" and "doing the wrong thing", in an abject mistranslation of how ethical choices correspond to financial cost. As long as privacy-friendly data treatment proves more expensive or complicated than readily available alternatives, economic imperatives will continue to outrank their legal counterparts. We examined existing legislation with the aim of facilitating mutually beneficial interactions, rather than more narrowly focusing on the prevention of undesired behaviors. In this article, we propose the implementation of evolvable trust systems as a scalable alternative to the omnipresent yet deeply broken delusion of ill-informed consent. We describe personalized, technology-assisted legal processes for initiating and maintaining long-term trust relationships, which enable parties to reliably and sustainably exchange data, goods, and services. Our proposal encourages a redirection of additional efforts towards the techno-legal alignment of economical incentives with societal ones, reminding us that - while trust remains an inherently human concept - technology can support people in evolving and scaling their relationships to meet the increasingly complex demands of current and future data landscapes.
SRPG: Semantically Reconstructed Privacy Guard for Zero-Trust Privacy in Educational Multi-Agent Systems
Venue: arXiv cs.MA 2025
Year: 2025

Multi-Agent Systems (MAS) with large language models (LLMs) enable personalized education but risk leaking minors personally identifiable information (PII) via unstructured dialogue. Existing privacy methods struggle to balance security and utility: role-based access control fails on unstructured text, while naive masking destroys pedagogical context. We propose SRPG, a privacy guard for educational MAS, using a Dual-Stream Reconstruction Mechanism: a strict sanitization stream ensures zero PII leakage, and a context reconstruction stream (LLM driven) recovers mathematical logic. This decouples instructional content from private data, preserving teaching efficacy. Tests on MathDial show SRPG works across models; with GPT-4o, it achieves 0.0000 Attack Success Rate (ASR) (zero leakage) and 0.8267 Exact Match, far outperforming the zero trust Pure LLM baseline (0.2138). SRPG effectively protects minors privacy without sacrificing mathematical instructional quality.
Quantum Topological Graph Neural Networks for Detecting Complex Fraud Patterns
Venue: arXiv cs.LG 2025
Year: 2025

We propose a novel QTGNN framework for detecting fraudulent transactions in large-scale financial networks. By integrating quantum embedding, variational graph convolutions, and topological data analysis, QTGNN captures complex transaction dynamics and structural anomalies indicative of fraud. The methodology includes quantum data embedding with entanglement enhancement, variational quantum graph convolutions with non-linear dynamics, extraction of higher-order topological invariants, hybrid quantum-classical anomaly learning with adaptive optimization, and interpretable decision-making via topological attribution. Rigorous convergence guarantees ensure stable training on noisy intermediate-scale quantum (NISQ) devices, while stability of topological signatures provides robust fraud detection. Optimized for NISQ hardware with circuit simplifications and graph sampling, the framework scales to large transaction networks. Simulations on financial datasets, such as PaySim and Elliptic, benchmark QTGNN against classical and quantum baselines, using metrics like ROC-AUC, precision, and false positive rate. An ablation study evaluates the contributions of quantum embeddings, topological features, non-linear channels, and hybrid learning. QTGNN offers a theoretically sound, interpretable, and practical solution for financial fraud detection, bridging quantum machine learning, graph theory, and topological analysis.
On the Challenges of Energy-Efficiency Analysis in HPC Systems: Evaluating Synthetic Benchmarks and Gromacs
Venue: arXiv cs.DC 2025
Year: 2025

This paper discusses the challenges encountered when analyzing the energy efficiency of synthetic benchmarks and the Gromacs package on the Fritz and Alex HPC clusters. Experiments were conducted using MPI parallelism on full sockets of Intel Ice Lake and Sapphire Rapids CPUs, as well as Nvidia A40 and A100 GPUs. The metrics and measurements obtained with the Likwid and Nvidia profiling tools are presented, along with the results. The challenges and pitfalls encountered during experimentation and analysis are revealed and discussed. Best practices for future energy efficiency analysis studies are suggested.
Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing
Venue: arXiv cs.IT 2025
Year: 2025

Over-the-Air Federated Learning (AirFL) is an emerging paradigm that tightly integrates wireless signal processing and distributed machine learning to enable scalable AI at the network edge. By leveraging the superposition property of wireless signals, AirFL performs communication and model aggregation of the learning process simultaneously, significantly reducing latency, bandwidth, and energy consumption. This article offers a tutorial treatment of AirFL, presenting a novel classification into three design approaches: CSIT-aware, blind, and weighted AirFL. We provide a comprehensive guide to theoretical foundations, performance analysis, complexity considerations, practical limitations, and prospective research directions.
Tutorial on Large Language Model-Enhanced Reinforcement Learning for Wireless Networks
Venue: arXiv cs.NI 2025
Year: 2025

Reinforcement Learning (RL) has shown remarkable success in enabling adaptive and data-driven optimization for various applications in wireless networks. However, classical RL suffers from limitations in generalization, learning feedback, interpretability, and sample efficiency in dynamic wireless environments. Large Language Models (LLMs) have emerged as a transformative Artificial Intelligence (AI) paradigm with exceptional capabilities in knowledge generalization, contextual reasoning, and interactive generation, which have demonstrated strong potential to enhance classical RL. This paper serves as a comprehensive tutorial on LLM-enhanced RL for wireless networks. We propose a taxonomy to categorize the roles of LLMs into four critical functions: state perceiver, reward designer, decision-maker, and generator. Then, we review existing studies exploring how each role of LLMs enhances different stages of the RL pipeline. Moreover, we provide a series of case studies to illustrate how to design and apply LLM-enhanced RL in low-altitude economy networking, vehicular networks, and space-air-ground integrated networks. Finally, we conclude with a discussion on potential future directions for LLM-enhanced RL and offer insights into its future development in wireless networks.